Keywords
Scenario discovery; Diversity; Socioeconomic pathways; Emissions scenarios; Database
Location
Session D5: Advancing in Environmental Decision Making Under Deep Uncertainty: Emerging Tools and Challenges
Start Date
12-7-2016 10:50 AM
End Date
12-7-2016 11:10 AM
Abstract
The Shared Socio-economic Pathways, or SSPs, are the next generation of socio-economic scenarios following the SRES. The SSP framework recognizes that different socio-economic conditions may lead to similar levels of emissions, or radiative forcing. This implies that any given level of radiative forcing may have very different socio-economic impacts depending on the conditions associated with it. To uncover different socio-economic conditions associated with any level of radiative forcing, we propose a methodology based on “scenario discovery” cluster analysis. With a database of hundreds of scenarios, we demonstrate how this method can identify very different groups of socio-economic scenarios sharing common CO2 emissions outcomes. We find that high emissions scenarios can occur under conditions of either high or low GDP per capita growth. We also find that for the high per capita GDP and high emissions scenarios, high productivity growth and catch-up are not necessarily required.
Included in
Civil Engineering Commons, Data Storage Systems Commons, Environmental Engineering Commons, Hydraulic Engineering Commons, Other Civil and Environmental Engineering Commons
CO2 emissions scenarios from a database of diverse socio-economic pathways
Session D5: Advancing in Environmental Decision Making Under Deep Uncertainty: Emerging Tools and Challenges
The Shared Socio-economic Pathways, or SSPs, are the next generation of socio-economic scenarios following the SRES. The SSP framework recognizes that different socio-economic conditions may lead to similar levels of emissions, or radiative forcing. This implies that any given level of radiative forcing may have very different socio-economic impacts depending on the conditions associated with it. To uncover different socio-economic conditions associated with any level of radiative forcing, we propose a methodology based on “scenario discovery” cluster analysis. With a database of hundreds of scenarios, we demonstrate how this method can identify very different groups of socio-economic scenarios sharing common CO2 emissions outcomes. We find that high emissions scenarios can occur under conditions of either high or low GDP per capita growth. We also find that for the high per capita GDP and high emissions scenarios, high productivity growth and catch-up are not necessarily required.
